Mechanistic Classification of Unbound Materials
Bibliographic record
Abstract
The effectiveness of an approach, based on the use of default resilient modulus (M r ) values in providing input to the Mechanistic–Empirical Pavement Design Guide (MEPDG) developed under the NCHRP 1–37A project, was evaluated. The process is intended to assist users of the guide in estimating the M r of an unbound material based on its AASHTO soil class. Assessment conducted at the National Research Council Canada (NRC) involved comparing values of the parameter determined in the laboratory using the AASHTO protocol with default values included in MEPDG. Comparison revealed that the proposed default values frequently overestimate the value of the M r parameter, hence yielding inadequate layer design. Examination of the AASHTO M r test revealed that the procedure does not account for permanent deformations that accumulate during the laboratory test. These findings prompted the NRC team to develop a different approach for estimating the M r parameter. The new approach yielded a mechanistic soil classification system based on the material response captured in the laboratory. A practical procedure for implementing the new mechanistic classification system was facilitated by establishing correlations between physical and mechanical properties of the materials. These correlations were found to be more accurate in estimating the M r parameter and were expected to produce better design when used as input for running the MEPDG model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".